Recent research reveals significant flaws in the narrative surrounding Large Language Models (LLMs) and their purported expertise in knowledge economy tasks. The primary issue lies in the benchmarking tasks used to evaluate LLM performance, which often measure success based on content directly included in the models' training data1. This limitation raises concerns about the true capabilities of LLMs, as they may not be able to generalize knowledge or perform well in real-world scenarios. The overestimation of LLM capabilities has significant implications for various domains, including policy, security, and workforce dynamics. As LLMs are increasingly integrated into critical systems, a more nuanced understanding of their limitations is necessary to mitigate potential risks. The flawed narrative surrounding LLMs matters to practitioners, as it can lead to misplaced trust in these models and ultimately compromise the security and reliability of systems that rely on them.